
'''simple docstring'''

import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation

_UpperCAmelCase :   Any			    =	logging.get_logger(__name__)


_UpperCAmelCase :   List[str]			    =	{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

_UpperCAmelCase :   Union[str, Any]			    =	{
    'vocab_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'},
    'merges_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'},
    'tokenizer_config_file': {
        'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'
    },
}

_UpperCAmelCase :   List[Any]			    =	{'facebook/blenderbot-3B': 128}







@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def     snake_case__						(   )			->   Dict:
		_UpperCamelCase							:			Any     =   (
		    list(range(ord('''!'''						)      ,ord('''~'''						) + 1						)						) + list(range(ord('''¡'''						)      ,ord('''¬'''						) + 1						)						) + list(range(ord('''®'''						)      ,ord('''ÿ'''						) + 1						)						)
		)
		_UpperCamelCase							:			Dict     =   bs[:]
		_UpperCamelCase							:			Any     =   0
		for b in range(2**8						):
				if b not in bs:
						bs.append(_A						)
						cs.append(2**8 + n						)
						n += 1
		_UpperCamelCase							:			Any     =   [chr(_A						) for n in cs]
		return dict(zip(_A      ,_A						)						)







def     snake_case__						(   UpperCamelCase						)			->   Union[str, Any]:
		_UpperCamelCase							:			Tuple     =   set()
		_UpperCamelCase							:			str     =   word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)						)
				_UpperCamelCase							:			int     =   char
		return pairs


class       UpperCAmelCase					(       lowerCamelCase__			):





		"""simple docstring"""





		A__	: List[str]    =     VOCAB_FILES_NAMES
		A__	: List[str]    =     PRETRAINED_VOCAB_FILES_MAP
		A__	: List[str]    =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		A__	: List[str]    =     ['input_ids', 'attention_mask']
		def __init__(     self	,       _snake_case	,       _snake_case	,       _snake_case="replace"	,       _snake_case="<s>"	,       _snake_case="</s>"	,       _snake_case="</s>"	,       _snake_case="<s>"	,       _snake_case="<unk>"	,       _snake_case="<pad>"	,       _snake_case="<mask>"	,       _snake_case=False	,       **_snake_case	,       )					->     List[str]:
				_UpperCamelCase							:			Dict     =   AddedToken(__snake_case	,       lstrip=__snake_case	,       rstrip=__snake_case  ) if isinstance(__snake_case	,       __snake_case  ) else bos_token
				_UpperCamelCase							:			Dict     =   AddedToken(__snake_case	,       lstrip=__snake_case	,       rstrip=__snake_case  ) if isinstance(__snake_case	,       __snake_case  ) else eos_token
				_UpperCamelCase							:			Any     =   AddedToken(__snake_case	,       lstrip=__snake_case	,       rstrip=__snake_case  ) if isinstance(__snake_case	,       __snake_case  ) else sep_token
				_UpperCamelCase							:			Tuple     =   AddedToken(__snake_case	,       lstrip=__snake_case	,       rstrip=__snake_case  ) if isinstance(__snake_case	,       __snake_case  ) else cls_token
				_UpperCamelCase							:			List[str]     =   AddedToken(__snake_case	,       lstrip=__snake_case	,       rstrip=__snake_case  ) if isinstance(__snake_case	,       __snake_case  ) else unk_token
				_UpperCamelCase							:			Tuple     =   AddedToken(__snake_case	,       lstrip=__snake_case	,       rstrip=__snake_case  ) if isinstance(__snake_case	,       __snake_case  ) else pad_token

				# Mask token behave like a normal word, i.e. include the space before it
				_UpperCamelCase							:			Union[str, Any]     =   AddedToken(__snake_case	,       lstrip=__snake_case	,       rstrip=__snake_case  ) if isinstance(__snake_case	,       __snake_case  ) else mask_token

				super().__init__(
				    errors=__snake_case	,       bos_token=__snake_case	,       eos_token=__snake_case	,       unk_token=__snake_case	,       sep_token=__snake_case	,       cls_token=__snake_case	,       pad_token=__snake_case	,       mask_token=__snake_case	,       add_prefix_space=__snake_case	,       **__snake_case	,       )

				with open(__snake_case	,       encoding='''utf-8'''  ) as vocab_handle:
						_UpperCamelCase							:			Any     =   json.load(__snake_case  )
				_UpperCamelCase							:			Tuple     =   {v: k for k, v in self.encoder.items()}
				_UpperCamelCase							:			str     =   errors  # how to handle errors in decoding
				_UpperCamelCase							:			Any     =   bytes_to_unicode()
				_UpperCamelCase							:			str     =   {v: k for k, v in self.byte_encoder.items()}
				with open(__snake_case	,       encoding='''utf-8'''  ) as merges_handle:
						_UpperCamelCase							:			Any     =   merges_handle.read().split('''\n'''  )[1:-1]
				_UpperCamelCase							:			Any     =   [tuple(merge.split()  ) for merge in bpe_merges]
				_UpperCamelCase							:			List[Any]     =   dict(zip(__snake_case	,       range(len(__snake_case  )  )  )  )
				_UpperCamelCase							:			str     =   {}
				_UpperCamelCase							:			str     =   add_prefix_space

				# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
				_UpperCamelCase							:			List[Any]     =   re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''  )
		@property
		# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
		def _lowercase	(     self  )					->     List[str]:
				return len(self.encoder  )
		def _lowercase	(     self  )					->     Dict:
				return dict(self.encoder	,       **self.added_tokens_encoder  )
		def _lowercase	(     self	,       _snake_case  )					->     Tuple:
				if token in self.cache:
						return self.cache[token]
				_UpperCamelCase							:			str     =   tuple(__snake_case  )
				_UpperCamelCase							:			Union[str, Any]     =   get_pairs(__snake_case  )

				if not pairs:
						return token

				while True:
						_UpperCamelCase							:			int     =   min(__snake_case	,       key=lambda _snake_case  : self.bpe_ranks.get(__snake_case	,       float('''inf'''  )  )  )
						if bigram not in self.bpe_ranks:
								break
						_UpperCamelCase							:			Optional[Any]     =   bigram
						_UpperCamelCase							:			Optional[Any]     =   []
						_UpperCamelCase							:			Tuple     =   0
						while i < len(__snake_case  ):
								try:
										_UpperCamelCase							:			List[Any]     =   word.index(__snake_case	,       __snake_case  )
								except ValueError:
										new_word.extend(word[i:]  )
										break
								else:
										new_word.extend(word[i:j]  )
										_UpperCamelCase							:			Optional[int]     =   j

								if word[i] == first and i < len(__snake_case  ) - 1 and word[i + 1] == second:
										new_word.append(first + second  )
										i += 2
								else:
										new_word.append(word[i]  )
										i += 1
						_UpperCamelCase							:			Optional[int]     =   tuple(__snake_case  )
						_UpperCamelCase							:			int     =   new_word
						if len(__snake_case  ) == 1:
								break
						else:
								_UpperCamelCase							:			List[str]     =   get_pairs(__snake_case  )
				_UpperCamelCase							:			Optional[Any]     =   ' '.join(__snake_case  )
				_UpperCamelCase							:			List[str]     =   word
				return word
		def _lowercase	(     self	,       _snake_case  )					->     Optional[int]:
				_UpperCamelCase							:			Optional[Any]     =   []
				for token in re.findall(self.pat	,       __snake_case  ):
						_UpperCamelCase							:			Tuple     =   ''.join(
						    self.byte_encoder[b] for b in token.encode('''utf-8'''  )  )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
						bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__snake_case  ).split(''' '''  )  )
				return bpe_tokens
		def _lowercase	(     self	,       _snake_case  )					->     List[str]:
				return self.encoder.get(__snake_case	,       self.encoder.get(self.unk_token  )  )
		def _lowercase	(     self	,       _snake_case  )					->     Any:
				return self.decoder.get(__snake_case  )
		def _lowercase	(     self	,       _snake_case  )					->     List[Any]:
				_UpperCamelCase							:			Optional[Any]     =   ''.join(__snake_case  )
				_UpperCamelCase							:			int     =   bytearray([self.byte_decoder[c] for c in text]  ).decode('''utf-8'''	,       errors=self.errors  )
				return text
		def _lowercase	(     self	,       _snake_case	,       _snake_case = None  )					->     List[Any]:
				if not os.path.isdir(__snake_case  ):
						logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
						return
				_UpperCamelCase							:			List[str]     =   os.path.join(
				    __snake_case	,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )
				_UpperCamelCase							:			Optional[int]     =   os.path.join(
				    __snake_case	,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']  )

				with open(__snake_case	,       '''w'''	,       encoding='''utf-8'''  ) as f:
						f.write(json.dumps(self.encoder	,       indent=2	,       sort_keys=__snake_case	,       ensure_ascii=__snake_case  ) + '''\n'''  )

				_UpperCamelCase							:			Dict     =   0
				with open(__snake_case	,       '''w'''	,       encoding='''utf-8'''  ) as writer:
						writer.write('''#version: 0.2\n'''  )
						for bpe_tokens, token_index in sorted(self.bpe_ranks.items()	,       key=lambda _snake_case  : kv[1]  ):
								if index != token_index:
										logger.warning(
										    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
										    ''' Please check that the tokenizer is not corrupted!'''  )
										_UpperCamelCase							:			List[Any]     =   token_index
								writer.write(''' '''.join(__snake_case  ) + '''\n'''  )
								index += 1

				return vocab_file, merge_file
		def _lowercase	(     self	,       _snake_case	,       _snake_case = None	,       _snake_case = False  )					->     Dict:
				if already_has_special_tokens:
						return super().get_special_tokens_mask(
						    token_ids_a=__snake_case	,       token_ids_a=__snake_case	,       already_has_special_tokens=__snake_case  )

				if token_ids_a is None:
						return [1] + ([0] * len(__snake_case  )) + [1]
				return [1] + ([0] * len(__snake_case  )) + [1, 1] + ([0] * len(__snake_case  )) + [1]
		def _lowercase	(     self	,       _snake_case	,       _snake_case = None  )					->     Union[str, Any]:
				_UpperCamelCase							:			Optional[Any]     =   [self.sep_token_id]
				_UpperCamelCase							:			Dict     =   [self.cls_token_id]

				if token_ids_a is None:
						return len(cls + token_ids_a + sep  ) * [0]
				return len(cls + token_ids_a + sep + sep + token_ids_a + sep  ) * [0]
		def _lowercase	(     self	,       _snake_case	,       _snake_case=False	,       **_snake_case  )					->     Optional[Any]:
				_UpperCamelCase							:			str     =   kwargs.pop('''add_prefix_space'''	,       self.add_prefix_space  )
				if (is_split_into_words or add_prefix_space) and (len(__snake_case  ) > 0 and not text[0].isspace()):
						_UpperCamelCase							:			Optional[Any]     =   ' ' + text
				return (text, kwargs)
		def _lowercase	(     self	,       _snake_case	,       _snake_case = None  )					->     int:
				return token_ids_a + [self.eos_token_id]




		def _lowercase	(     self	,       _snake_case  )					->     Dict:
				_UpperCamelCase							:			Dict     =   []
				for is_user, text in conversation.iter_texts():
						if is_user:
								# We need to space prefix as it's being done within blenderbot
								inputs.append(''' ''' + text  )
						else:
								# Generated responses should contain them already.
								inputs.append(__snake_case  )

				_UpperCamelCase							:			List[Any]     =   '  '.join(__snake_case  )
				_UpperCamelCase							:			List[Any]     =   self.encode(__snake_case  )
				if len(__snake_case  ) > self.model_max_length:
						_UpperCamelCase							:			int     =   input_ids[-self.model_max_length :]
						logger.warning(F'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'''  )
				return input_ids










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase:   List[Any]					=		{'configuration_reformer': ['REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ReformerConfig']}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   Dict					=		['ReformerTokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   Tuple					=		['ReformerTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   List[Any]					=		[
         'REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'ReformerAttention',
         'ReformerForMaskedLM',
         'ReformerForQuestionAnswering',
         'ReformerForSequenceClassification',
         'ReformerLayer',
         'ReformerModel',
         'ReformerModelWithLMHead',
         'ReformerPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_reformer import ReformerTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_reformer_fast import ReformerTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_reformer import (
              REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              ReformerAttention,
              ReformerForMaskedLM,
              ReformerForQuestionAnswering,
              ReformerForSequenceClassification,
              ReformerLayer,
              ReformerModel,
              ReformerModelWithLMHead,
              ReformerPreTrainedModel,
          )

else:
     import sys

     lowerCAmelCase:   str					=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)